More and more devices get equipped with sensors and get connected. The sensor data from these devices may be analyzed and used to make predictions and/or construct models. These models convert sensor data into information and knowledge, and add value. For example, in an activity monitoring bracelet models and algorithms are used to convert the sensor data from the motion sensors in the device into the number of steps the user/wearer takes and the distance he or she walks. This information of the number of steps and distance has more value to the user than the raw sensor signals. Determining if this number of steps is sufficient according to the user's lifestyle is knowledge. FIG. 1 shows a sensor, data, information, knowledge and wisdom (S-DIKW) pyramid where the value increases from the bottom to the top.
The models and algorithms that convert sensor data into information and knowledge take time and manpower to develop. For example, to determine the relation between the motion sensor signals and number of steps it has to be determined how to detect a step based on the raw sensor signals, and how the step length depends on the characteristics of the user, such as e.g. his or her height or style/speed of walking. In another example, an electronic tennis racket is equipped with sensors that convert the motion of the racket into a type of tennis swing using a pre-defined model. This model is based on an extended testing period with various tennis rackets and players. This makes developing a model very cost and labor intensive. By obtaining more sensor data from more players and rackets, analysis of the sensor data may be used to make a better model or better algorithms.
Often the models and algorithms are developed by specialized companies. An alternative way to develop models is to use the wisdom of the crowd, by having a crowd of people giving each their (small piece of) knowledge. In other words, by gathering the sensor data from many users, pattern or relations in the data can be investigated to determine the algorithms and models in a more automated manner. Many companies already exist that collect this data, often referred to as ‘big data’, and convert the data into value for the company. In almost all cases, the user who provided the data does not have any view in what is done with his or her data and how the big data company makes any profit. Moreover, the users and data providers almost never share in the profit these companies make through the added value.
In this disclosure a method and system are proposed where the user is in control of the sensor data he or she provides and the potential added value that can be generated based on this data. This means that the user can determine what happens to the data and that the user will receive any monetary benefit or other compensation that can be made based on the provided data. The user is assisted in these tasks by a service provider who helps the user construct a user profile based on the sensor data, and obtain monetizing options based on this profile and depending on privacy data, which may include the privacy settings of the user. FIG. 2 shows the flow of how the data produced by a user is converted into a user profile by a service provider, and how this profile may then be used to obtain compensation. The user profile may be consider an asset, and the user is in control on how to use this asset and convert it into compensation by sharing/exchanging/selling the profile information, or keeping the profile information private depending on his or her privacy ethics. The user profile has a dynamic character, which means that the user has to continue to provide (sensor) data to maintain his or her profile and the related monetizing options.
As will be described in the following materials, this disclosure satisfies these and other needs.